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  <h1>Source code for torch.quantization.fake_quantize</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">absolute_import</span><span class="p">,</span> <span class="n">division</span><span class="p">,</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">unicode_literals</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">Module</span>
<span class="kn">from</span> <span class="nn">.observer</span> <span class="kn">import</span> <span class="n">MovingAverageMinMaxObserver</span><span class="p">,</span> <span class="n">HistogramObserver</span><span class="p">,</span> <span class="n">MovingAveragePerChannelMinMaxObserver</span><span class="p">,</span> <span class="n">_with_args</span>

<div class="viewcode-block" id="FakeQuantize"><a class="viewcode-back" href="../../../quantization.html#torch.quantization.FakeQuantize">[docs]</a><span class="k">class</span> <span class="nc">FakeQuantize</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot; Simulate the quantize and dequantize operations in training time.</span>
<span class="sd">    The output of this module is given by</span>

<span class="sd">    x_out = (clamp(round(x/scale + zero_point), quant_min, quant_max)-zero_point)*scale</span>



<span class="sd">    * :attr:`scale` defines the scale factor used for quantization.</span>

<span class="sd">    * :attr:`zero_point` specifies the quantized value to which 0 in floating point maps to</span>

<span class="sd">    * :attr:`quant_min` specifies the minimum allowable quantized value.</span>

<span class="sd">    * :attr:`quant_max` specifies the maximum allowable quantized value.</span>

<span class="sd">    * :attr:`fake_quant_enable` controls the application of fake quantization on tensors, note that</span>
<span class="sd">      statistics can still be updated.</span>

<span class="sd">    * :attr:`observer_enable` controls statistics collection on tensors</span>

<span class="sd">    * :attr:`dtype` specifies the quantized dtype that is being emulated with fake-quantization,</span>
<span class="sd">                    allowable values are torch.qint8 and torch.quint8. The values of quant_min and</span>
<span class="sd">                    quant_max should be chosen to be consistent with the dtype</span>


<span class="sd">    Args:</span>
<span class="sd">        observer (module): Module for observing statistics on input tensors and calculating scale</span>
<span class="sd">                           and zero-point.</span>
<span class="sd">        quant_min (int): The minimum allowable quantized value.</span>
<span class="sd">        quant_max (int): The maximum allowable quantized value.</span>
<span class="sd">        observer_kwargs (optional): Arguments for the observer module</span>

<span class="sd">    Attributes:</span>
<span class="sd">        observer (Module): User provided module that collects statistics on the input tensor and</span>
<span class="sd">                           provides a method to calculate scale and zero-point.</span>

<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">observer</span><span class="o">=</span><span class="n">MovingAverageMinMaxObserver</span><span class="p">,</span> <span class="n">quant_min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">quant_max</span><span class="o">=</span><span class="mi">255</span><span class="p">,</span> <span class="o">**</span><span class="n">observer_kwargs</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">FakeQuantize</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="k">assert</span> <span class="n">quant_min</span> <span class="o">&lt;=</span> <span class="n">quant_max</span><span class="p">,</span> \
            <span class="s1">&#39;quant_min must be less than or equal to quant_max&#39;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">quant_min</span> <span class="o">=</span> <span class="n">quant_min</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">quant_max</span> <span class="o">=</span> <span class="n">quant_max</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">fake_quant_enabled</span> <span class="o">=</span> <span class="kc">True</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">observer_enabled</span> <span class="o">=</span> <span class="kc">True</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">activation_post_process</span> <span class="o">=</span> <span class="n">observer</span><span class="p">(</span><span class="o">**</span><span class="n">observer_kwargs</span><span class="p">)</span>
        <span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">iinfo</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">activation_post_process</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span><span class="o">.</span><span class="n">min</span> <span class="o">&lt;=</span> <span class="n">quant_min</span><span class="p">,</span> <span class="s1">&#39;quant_min out of bound&#39;</span>
        <span class="k">assert</span> <span class="n">quant_max</span> <span class="o">&lt;=</span> <span class="n">torch</span><span class="o">.</span><span class="n">iinfo</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">activation_post_process</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span><span class="o">.</span><span class="n">max</span><span class="p">,</span> <span class="s1">&#39;quant_max out of bound&#39;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span><span class="s1">&#39;scale&#39;</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.0</span><span class="p">]))</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span><span class="s1">&#39;zero_point&#39;</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">0</span><span class="p">]))</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">activation_post_process</span><span class="o">.</span><span class="n">dtype</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">qscheme</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">activation_post_process</span><span class="o">.</span><span class="n">qscheme</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">ch_axis</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">activation_post_process</span><span class="o">.</span><span class="n">ch_axis</span> <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">activation_post_process</span><span class="p">,</span> <span class="s1">&#39;ch_axis&#39;</span><span class="p">)</span> <span class="k">else</span> <span class="kc">None</span>

    <span class="k">def</span> <span class="nf">enable_fake_quant</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">enabled</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">fake_quant_enabled</span> <span class="o">=</span> <span class="n">enabled</span>
        <span class="k">return</span> <span class="bp">self</span>

    <span class="k">def</span> <span class="nf">disable_fake_quant</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">enable_fake_quant</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">enable_observer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">enabled</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">observer_enabled</span> <span class="o">=</span> <span class="n">enabled</span>
        <span class="k">return</span> <span class="bp">self</span>

    <span class="k">def</span> <span class="nf">disable_observer</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">enable_observer</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">calculate_qparams</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">activation_post_process</span><span class="o">.</span><span class="n">calculate_qparams</span><span class="p">()</span>

    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">observer_enabled</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">activation_post_process</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">detach</span><span class="p">())</span>
            <span class="n">_scale</span><span class="p">,</span> <span class="n">_zero_point</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">calculate_qparams</span><span class="p">()</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">scale</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">zero_point</span> <span class="o">=</span> <span class="n">_scale</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">scale</span><span class="o">.</span><span class="n">device</span><span class="p">),</span> <span class="n">_zero_point</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">zero_point</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">fake_quant_enabled</span><span class="p">:</span>
            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">qscheme</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">per_channel_symmetric</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">qscheme</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">per_channel_affine</span><span class="p">:</span>
                <span class="n">X</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">fake_quantize_per_channel_affine</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">zero_point</span><span class="p">,</span>
                                                           <span class="bp">self</span><span class="o">.</span><span class="n">ch_axis</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">quant_min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">quant_max</span><span class="p">)</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="n">X</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">fake_quantize_per_tensor_affine</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">scale</span><span class="p">),</span>
                                                          <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">zero_point</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">quant_min</span><span class="p">,</span>
                                                          <span class="bp">self</span><span class="o">.</span><span class="n">quant_max</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">X</span>

    <span class="n">with_args</span> <span class="o">=</span> <span class="nb">classmethod</span><span class="p">(</span><span class="n">_with_args</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">extra_repr</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="s1">&#39;fake_quant_enabled=</span><span class="si">{}</span><span class="s1">, observer_enabled=</span><span class="si">{}</span><span class="s1">,</span><span class="se">\</span>
<span class="s1">            scale=</span><span class="si">{}</span><span class="s1">, zero_point=</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">fake_quant_enabled</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">observer_enabled</span><span class="p">,</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">scale</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">zero_point</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">_save_to_state_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">destination</span><span class="p">,</span> <span class="n">prefix</span><span class="p">,</span> <span class="n">keep_vars</span><span class="p">):</span>
        <span class="c1"># We cannot currently register scalar values as buffers, so need to manually</span>
        <span class="c1"># specify serialization here.</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">FakeQuantize</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">_save_to_state_dict</span><span class="p">(</span><span class="n">destination</span><span class="p">,</span> <span class="n">prefix</span><span class="p">,</span> <span class="n">keep_vars</span><span class="p">)</span>
        <span class="n">destination</span><span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="s1">&#39;scale&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span>
        <span class="n">destination</span><span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="s1">&#39;zero_point&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">zero_point</span>

    <span class="k">def</span> <span class="nf">_load_from_state_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">,</span> <span class="n">prefix</span><span class="p">,</span> <span class="n">local_metadata</span><span class="p">,</span> <span class="n">strict</span><span class="p">,</span>
                              <span class="n">missing_keys</span><span class="p">,</span> <span class="n">unexpected_keys</span><span class="p">,</span> <span class="n">error_msgs</span><span class="p">):</span>
        <span class="c1"># Removing this function throws an error that the the size of the loaded tensor does not match the original size</span>
        <span class="c1"># i.e., These buffers start out with numel 0 and become numel 1 once they have their first forward pass.</span>
        <span class="n">local_state</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;scale&#39;</span><span class="p">,</span> <span class="s1">&#39;zero_point&#39;</span><span class="p">]</span>
        <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">local_state</span><span class="p">:</span>
            <span class="n">key</span> <span class="o">=</span> <span class="n">prefix</span> <span class="o">+</span> <span class="n">name</span>
            <span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">state_dict</span><span class="p">:</span>
                <span class="n">val</span> <span class="o">=</span> <span class="n">state_dict</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
                <span class="nb">setattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">val</span><span class="p">)</span>
            <span class="k">elif</span> <span class="n">strict</span><span class="p">:</span>
                <span class="n">missing_keys</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">FakeQuantize</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">_load_from_state_dict</span><span class="p">(</span><span class="n">state_dict</span><span class="p">,</span> <span class="n">prefix</span><span class="p">,</span> <span class="n">local_metadata</span><span class="p">,</span> <span class="n">strict</span><span class="p">,</span>
                                                        <span class="n">missing_keys</span><span class="p">,</span> <span class="n">unexpected_keys</span><span class="p">,</span> <span class="n">error_msgs</span><span class="p">)</span></div>

<span class="n">default_fake_quant</span> <span class="o">=</span> <span class="n">FakeQuantize</span><span class="o">.</span><span class="n">with_args</span><span class="p">(</span><span class="n">observer</span><span class="o">=</span><span class="n">MovingAverageMinMaxObserver</span><span class="p">,</span> <span class="n">quant_min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">quant_max</span><span class="o">=</span><span class="mi">255</span><span class="p">,</span>
                                            <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">quint8</span><span class="p">,</span> <span class="n">qscheme</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">per_tensor_affine</span><span class="p">,</span> <span class="n">reduce_range</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">default_weight_fake_quant</span> <span class="o">=</span> <span class="n">FakeQuantize</span><span class="o">.</span><span class="n">with_args</span><span class="p">(</span><span class="n">observer</span><span class="o">=</span><span class="n">MovingAverageMinMaxObserver</span><span class="p">,</span> <span class="n">quant_min</span><span class="o">=-</span><span class="mi">128</span><span class="p">,</span> <span class="n">quant_max</span><span class="o">=</span><span class="mi">127</span><span class="p">,</span>
                                                   <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">qint8</span><span class="p">,</span> <span class="n">qscheme</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">per_tensor_symmetric</span><span class="p">,</span> <span class="n">reduce_range</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>

<span class="n">default_per_channel_weight_fake_quant</span> <span class="o">=</span> <span class="n">FakeQuantize</span><span class="o">.</span><span class="n">with_args</span><span class="p">(</span><span class="n">observer</span><span class="o">=</span><span class="n">MovingAveragePerChannelMinMaxObserver</span><span class="p">,</span>
                                                               <span class="n">quant_min</span><span class="o">=-</span><span class="mi">128</span><span class="p">,</span>
                                                               <span class="n">quant_max</span><span class="o">=</span><span class="mi">127</span><span class="p">,</span>
                                                               <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">qint8</span><span class="p">,</span>
                                                               <span class="n">qscheme</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">per_channel_symmetric</span><span class="p">,</span>
                                                               <span class="n">reduce_range</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                                                               <span class="n">ch_axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">default_histogram_fake_quant</span> <span class="o">=</span> <span class="n">FakeQuantize</span><span class="o">.</span><span class="n">with_args</span><span class="p">(</span><span class="n">observer</span><span class="o">=</span><span class="n">HistogramObserver</span><span class="p">,</span>
                                                      <span class="n">quant_min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
                                                      <span class="n">quant_max</span><span class="o">=</span><span class="mi">255</span><span class="p">,</span>
                                                      <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">quint8</span><span class="p">,</span>
                                                      <span class="n">qscheme</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">per_tensor_affine</span><span class="p">,</span>
                                                      <span class="n">reduce_range</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">disable_fake_quant</span><span class="p">(</span><span class="n">mod</span><span class="p">):</span>
    <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">mod</span><span class="p">)</span> <span class="o">==</span> <span class="n">FakeQuantize</span><span class="p">:</span>
        <span class="n">mod</span><span class="o">.</span><span class="n">disable_fake_quant</span><span class="p">()</span>

<span class="k">def</span> <span class="nf">enable_fake_quant</span><span class="p">(</span><span class="n">mod</span><span class="p">):</span>
    <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">mod</span><span class="p">)</span> <span class="o">==</span> <span class="n">FakeQuantize</span><span class="p">:</span>
        <span class="n">mod</span><span class="o">.</span><span class="n">enable_fake_quant</span><span class="p">()</span>

<span class="k">def</span> <span class="nf">disable_observer</span><span class="p">(</span><span class="n">mod</span><span class="p">):</span>
    <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">mod</span><span class="p">)</span> <span class="o">==</span> <span class="n">FakeQuantize</span><span class="p">:</span>
        <span class="n">mod</span><span class="o">.</span><span class="n">disable_observer</span><span class="p">()</span>

<span class="k">def</span> <span class="nf">enable_observer</span><span class="p">(</span><span class="n">mod</span><span class="p">):</span>
    <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">mod</span><span class="p">)</span> <span class="o">==</span> <span class="n">FakeQuantize</span><span class="p">:</span>
        <span class="n">mod</span><span class="o">.</span><span class="n">enable_observer</span><span class="p">()</span>
</pre></div>

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